Crime data

We combined information from the following sources with the Chicago PD’s online crime data.

Online data sources

• Crime data from the Chicago Online Data Portal
  • Covers incidents from Jan. 1, 2001 through Aug. 31, 2019.
  • One row per incident, regardless of the number of victims, except for homicides, which is one row per victim.
• Sunrise and sunset times for Chicago from sunrise_sunset.org to calculate the share of shootings that happen during the day.

Freedom of Information Law Request data

• All homicides, Jan. 1, 2001 through Aug. 31, 2019. This data includes the following information:
  • “Cleared Yes/No” and date cleared, and but not detailed case status.
  • Injury type, from which weapon is inferred.
  • Circumstance (Robbery, Drug, Gang, Sex, etc.)
  • Victim’s relationship to offender.
  • Victim name, age, gender, and race/ethnicity.
• All shootings (fatal and nonfatal), Jan. 1, 2010 through Aug. 31, 2019.
  • The only additional information, beyond what’s in the online data, is the detailed case status and that the incident was classified as a shooting.
• For sworn officers, current unit assignments and historical unit assignments.

Manual categorizations

We manually categorized a few fields to add additional information to the analysis:

• Location descriptions from the online data are manually categorized into four groups: “Inside”, “Outside”, “Unclear/Unspecified”, and “Vehicle/Transit/Boat”. Another field gives a more detailed categorization, indicating the type of location, such as “Residential”, “Commercial”, “School”, “Park”, etc.
• The IUCR crime codes and descriptions from the online data are used to derive the following information, using the Chicago PD’s Incident Reporting Guide and other references (see notes in “01A_Clean_Crime_Data.Rmd”):
  • Crime type, such as “Part I Violent” and “Narcotics - Possession, Purchase”.
  • Weapon type.
  • Whether the incident was a shooting (battery has IUCR codes that specify that the victim was shot).
  • Whether the victim was injured.
  • The type of victim (general, minor, police, etc.).
  • For drug and alcohol offenses, the type of drug, and whether the offense was related to possession/purchase or sale/manufacturing.
• Specific victim-offender relationships are categorized into “Romantic”, “Other Family”, “Otherwise Known”, “Stranger”, “Other”, and “Null/Unknown”. This information is only available for homicides.
• Added detective Area (North, Central, South), based on present-day jurisdictions. Detective areas were consolidated from five to three areas in 2012. The detective area field for incidents prior to 2012 may not be accurate for the incident at that time if it took place in a jurisdiction that was changed as a result of restructuring.
• Detective Area watch times added based on page 20 of PERF’s Homicide Clearance report, released October 2019. These are present-day watch times, and would not be accurate in instances in which the watch time has changed. The times are applied to all years in order to check for a change over a consistent set of time periods.
  • 1st watch begins 10 pm, 2nd watch begins 7 am, 3rd watch begins 3 pm.
  • North and Central Area detectives have 8.5-hour shifts, South Area detectives have 10-hour shifts.
  • These are present-day watch times, and the detective areas are

Inconsistencies in incident counts

We compared the incident and victim counts in the online data, our FOIA data, and the Chicago PD’s reported statistics:

• Online data homicide counts are close to the number of homicides reported to the FBI via Return-A.
• Aggravated assault counts fall short. This is likely because the online data has one record per incident, regardless of victim count, for all crimes besides homicides. Aggravated Assaults and rapes are reported to the FBI by victim.
• Robberies are reported to the FBI by incident, and the counts compare much more closely to the counts from the online data.

Police district demographic data

We estimated the demographics of Chicago’s police districts by doing a spatial join of the police district and census tract shapes.

Police district boundaries

Note on police districts

The Chicago PD closed the 13th, 21st and 23rd districts in 2012 as part of a cost-cutting plan. The closed districts are not used in the online data at all, including for the years when they were open, indicating that all incidents are located by their present-day district. To ensure accuracy, we limit the published portions of our district-level demographic analyses to the years starting in 2013.

• Current as of Dec. 19, 2012, from city’s online data portal
• Depreciated December 18, 2012, from city’s online data portal. (pdf map)

Police beats

• Current as of Dec. 19, 2012, from city’s online data portal.

Census Tracts

• 2010 Census Tracts, from city’s online data portal.
• 2000 Census Tracts, from city’s online data portal.

Census Bureau Stats

We infer population and other demographic counts for the years between the census by dividing the change evenly between each year. For example, if the total number of people in a district increased by 100 people between the 2010 and 2015 ACS population counts, we added 25 people to the 2011, 2012, 2013, and 2014 population totals.

• 2000 Census: Total population, race/ethnicity, poverty (used for citywide estimates)
• 2010 Census: Total population, race/ethnicity
• ACS 2010: Poverty, population earning $100K+
• ACS 2015: Poverty

Note on “Hispanic or Latino” descent

We used U.S. Census data on people who identify as “Hispanic or Latino” to calculate the racial and ethnic demographics of each district. The Trace uses “Latino” because fewer than 1 percent of those identifying as “Hispanic or Latino” in Cook County are of Spanish origin; most descend from Latin America and the Caribbean.

Reviewers

The following experts reviewed and provided feedback on our methodology and findings:

• Max Kapustin, senior researcher at the University of Chicago’s Crime Lab. (Bio)
• Christy E. Lopez, Georgetown Law. As deputy chief of the U.S. Department of Justice Civil Rights Division’s Special Litigation Section, she led the investigations into several police departments, including the Chicago Police Department. (Bio)
• Wesley G. Skogan, a professor at Northwestern University’s Institute for Policy Research. He most recently published the book, “Police and Community in Chicago”. (Bio)

Linear models

For the share of arrests that are for drug possession and purchase, the district’s share of residents who are black or Latino decreases in significance in the 2016-2018 time period.

doj_lm <- op_up_doj %>% 
  filter(year %in% c("2013-2015", "2016-2018")) %>%
  select(location_police_district, 
         year, 
         drug_poss_buy_share_allarrest, 
         shootings_arrest_rate,
         shootings_per_100,
         shootings_total,
         violent_arrest_rate,
         violent_per_100,
         violent_total,
         dist_percent_blk_hisp,
         dist_percent_white) %>%
  unique()

summary(lm(drug_poss_buy_share_allarrest ~ dist_percent_blk_hisp,
            data = (doj_lm %>%
                      filter(year == "2013-2015"))))

## 
## Call:
## lm(formula = drug_poss_buy_share_allarrest ~ dist_percent_blk_hisp, 
##     data = (doj_lm %>% filter(year == "2013-2015")))
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.110152 -0.039129 -0.005695  0.041053  0.141894 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.13063    0.03511   3.721 0.001351 ** 
## dist_percent_blk_hisp  0.22892    0.04951   4.624 0.000164 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06867 on 20 degrees of freedom
## Multiple R-squared:  0.5166, Adjusted R-squared:  0.4925 
## F-statistic: 21.38 on 1 and 20 DF,  p-value: 0.0001641

summary(lm(drug_poss_buy_share_allarrest ~ dist_percent_blk_hisp,
            data = (doj_lm %>%
                      filter(year == "2016-2018"))))

## 
## Call:
## lm(formula = drug_poss_buy_share_allarrest ~ dist_percent_blk_hisp, 
##     data = (doj_lm %>% filter(year == "2016-2018")))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.08145 -0.03523 -0.01152  0.01771  0.17134 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            0.05625    0.03216   1.749  0.09559 . 
## dist_percent_blk_hisp  0.13938    0.04558   3.058  0.00621 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0635 on 20 degrees of freedom
## Multiple R-squared:  0.3186, Adjusted R-squared:  0.2845 
## F-statistic:  9.35 on 1 and 20 DF,  p-value: 0.006211

For the share of shootings that have led to arrest, the district’s share of residents who are black or Latino increases in significance in the 2016-2018 time period.

summary(lm(shootings_arrest_rate ~ dist_percent_blk_hisp,
            data = (doj_lm %>%
                      filter(year == "2013-2015" & shootings_total >= 20))))

## 
## Call:
## lm(formula = shootings_arrest_rate ~ dist_percent_blk_hisp, data = (doj_lm %>% 
##     filter(year == "2013-2015" & shootings_total >= 20)))
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.075752 -0.015723 -0.009771  0.019802  0.102032 
## 
## Coefficients:
##                       Estimate Std. Error t value    Pr(>|t|)    
## (Intercept)            0.17712    0.02296   7.714 0.000000204 ***
## dist_percent_blk_hisp -0.06534    0.03238  -2.018      0.0572 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.04491 on 20 degrees of freedom
## Multiple R-squared:  0.1692, Adjusted R-squared:  0.1276 
## F-statistic: 4.072 on 1 and 20 DF,  p-value: 0.0572

summary(lm(shootings_arrest_rate ~ dist_percent_blk_hisp,
            data = (doj_lm %>%
                      filter(year == "2016-2018" & shootings_total >= 20))))

## 
## Call:
## lm(formula = shootings_arrest_rate ~ dist_percent_blk_hisp, data = (doj_lm %>% 
##     filter(year == "2016-2018" & shootings_total >= 20)))
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.085290 -0.015401  0.001766  0.022690  0.140806 
## 
## Coefficients:
##                       Estimate Std. Error t value     Pr(>|t|)    
## (Intercept)            0.21772    0.02421   8.994 0.0000000182 ***
## dist_percent_blk_hisp -0.15411    0.03431  -4.492     0.000223 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.04779 on 20 degrees of freedom
## Multiple R-squared:  0.5022, Adjusted R-squared:  0.4773 
## F-statistic: 20.18 on 1 and 20 DF,  p-value: 0.0002229

Detective areas current staffing

The department started increasing its detective ranks in 2017, and currently about 1,060 sworn officers are assigned to the detective areas. (More recent statistics for gang and narcotics units are not available because CPD withheld undercover officers from the current assignment data it provided to The Trace.)

emp_current %>%
  filter(unit_type_inferred == "Detective Area") %>%
  group_by(unit_description) %>%
  summarise(n = n()) %>%
  adorn_totals()

##          unit_description    n
##  DETECTIVE AREA - CENTRAL  402
##    DETECTIVE AREA - NORTH  336
##    DETECTIVE AREA - SOUTH  320
##                     Total 1058